9,819 research outputs found
PI-BA Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization
Bundle adjustment (BA) is a fundamental optimization technique used in many
crucial applications, including 3D scene reconstruction, robotic localization,
camera calibration, autonomous driving, space exploration, street view map
generation etc. Essentially, BA is a joint non-linear optimization problem, and
one which can consume a significant amount of time and power, especially for
large optimization problems. Previous approaches of optimizing BA performance
heavily rely on parallel processing or distributed computing, which trade
higher power consumption for higher performance. In this paper we propose
{\pi}-BA, the first hardware-software co-designed BA engine on an embedded
FPGA-SoC that exploits custom hardware for higher performance and power
efficiency. Specifically, based on our key observation that not all points
appear on all images in a BA problem, we designed and implemented a
Co-Observation Optimization technique to accelerate BA operations with
optimized usage of memory and computation resources. Experimental results
confirm that {\pi}-BA outperforms the existing software implementations in
terms of performance and power consumption.Comment: in Proceedings of IEEE FCCM 201
3D face tracking and multi-scale, spatio-temporal analysis of linguistically significant facial expressions and head positions in ASL
Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body. This poses a significant challenge for computer-based sign language recognition. Here, we present new methods for the recognition of nonmanual grammatical markers in American Sign Language (ASL) based on: (1) new 3D tracking methods for the estimation of 3D head pose and facial expressions to determine the relevant low-level features; (2) methods for higher-level analysis of component events (raised/lowered eyebrows, periodic head nods and head shakes) used in grammatical markings—with differentiation of temporal phases (onset, core, offset, where appropriate), analysis of their characteristic properties, and extraction of corresponding features; (3) a 2-level learning framework to combine lowand high-level features of differing spatio-temporal scales. This new approach achieves significantly better tracking and recognition results than our previous methods
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